An Atlas for Cardiac MRI Regional Wall Motion and Infarct Scoring

  • Pau Medrano-Gracia
  • Avan Suinesiaputra
  • Brett Cowan
  • David Bluemke
  • Alejandro Frangi
  • Daniel Lee
  • João Lima
  • Alistair Young
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7746)

Abstract

Regional wall motion and infarct scoring of MR images are routine clinical tools to grade performance and scarring in the heart. The aim of this paper is to provide a framework for automatic scoring to alert the diagnostician to potential regions of abnormality. We investigated different shape and motion configurations of a finite-element cardiac atlas of the left ventricle. Two patient populations were used: 300 asymptomatic volunteers and 105 patients with myocardial infarction, both randomly selected from the Cardiac Atlas Project database. Support vector machines were employed to estimate the boundaries between the asymptomatic control and patient groups for each of 16 standard anatomical regions in the heart. Ground truth visual wall motion scores from standard cines and infarct scoring from late enhancement were provided by experienced observers. From all configurations, end-systolic shape best predicted wall motion abnormalities (global accuracy 78%, positive predictive value 85%, specificity 91%, sensitivity 60%) and infarct scoring (74%, 72%, 91%, 44%). In conclusion, computer assisted wall motion and infarct scoring has the potential to provide robust identification of those segments requiring further clinical attention; in particular, the high specificity and relatively low sensitivity could help avoid unnecessary late gadolinium rescanning of patients.

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References

  1. 1.
    Altman, D., Bland, J.: Diagnostic tests 2: Predictive values. BMJ: British Medical Journal 309(6947), 102 (1994)CrossRefGoogle Scholar
  2. 2.
    Bild, D.E., Bluemke, D.A., Burke, G.L., et al.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002)CrossRefGoogle Scholar
  3. 3.
    Canty Jr., J.M., Fallavollita, J.A.: Hibernating myocardium. Journal of Nuclear Cardiology 12(1), 104–119 (2005)CrossRefGoogle Scholar
  4. 4.
    Cerqueira, M.D., Weissman, N.J., Dilsizian, V., et al.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105(4), 539–542 (2002)CrossRefGoogle Scholar
  5. 5.
    Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., Sitges, M., Bijnens, B., Frangi, A.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Medical Image Analysis (2011)Google Scholar
  6. 6.
    Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)MATHGoogle Scholar
  7. 7.
    Hoffmann, R., von Bardeleben, S., Kasprzak, J.D., et al.: Analysis of regional left ventricular function by cineventriculography, cardiac magnetic resonance imaging, and unenhanced and contrast-enhanced echocardiography: A multicenter comparison of methods. J. Am. Coll. Cardiol. 47(1), 121–128 (2006)CrossRefGoogle Scholar
  8. 8.
    Kadish, A.H., Bello, D., Finn, J.P., et al.: Rationale and design for the Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation (DETERMINE) trial. J. Cardiovasc. Electrophysiol. 20(9), 982–987 (2009)CrossRefGoogle Scholar
  9. 9.
    Lekadir, K., Keenan, N.G., Pennell, D.J., Yang, G.Z.: An inter-landmark approach to 4-D shape extraction and interpretation: application to myocardial motion assessment in MRI. IEEE Trans. Med. Imaging 30(1), 52–68 (2011)CrossRefGoogle Scholar
  10. 10.
    Ortiz-Pérez, J.T., Rodríguez, J., Meyers, S.N., et al.: Correspondence between the 17-segment model and coronary arterial anatomy using contrast-enhanced cardiac magnetic resonance imaging. JACC Cardiovasc. Imaging 1(3), 282–293 (2008)CrossRefGoogle Scholar
  11. 11.
    Punithakumar, K., Ben Ayed, I., Ross, I.G., et al.: Detection of left ventricular motion abnormality via information measures and bayesian filtering. IEEE Trans. Inf. Technol. Biomed. 14(4), 1106–1113 (2010)CrossRefGoogle Scholar
  12. 12.
    Reddy, G.P., Pujadas, S., Ordovas, K.G., Higgins, C.B.: MR imaging of ischemic heart disease. Magn. Reson. Imaging Clin. N. Am. 16(2), 201–212 (2008)CrossRefGoogle Scholar
  13. 13.
    Redheuil, A.B., Kachenoura, N., Laporte, R., et al.: Interobserver variability in assessing segmental function can be reduced by combining visual analysis of CMR cine sequences with corresponding parametric images of myocardial contraction. J. Cardiovasc. Magn. Reson. 9(6), 863–872 (2007)CrossRefGoogle Scholar
  14. 14.
    Suinesiaputra, A., Frangi, A.F., Kaandorp, T.A.M., et al.: Automated regional wall motion abnormality detection by combining rest and stress cardiac MRI: Correlation with contrast-enhanced MRI. J. Magn. Reson. Imaging 34(2), 270–278 (2011)CrossRefGoogle Scholar
  15. 15.
    Suinesiaputra, A., Frangi, A.F., Kaandorp, T.A.M., et al.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)CrossRefGoogle Scholar
  16. 16.
    Vapnik, V.: The nature of statistical learning theory. Springer (2000)Google Scholar
  17. 17.
    White, H., Norris, R., Brown, M., Brandt, P., Whitlock, R., Wild, C.: Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 76(1), 44–51 (1987)CrossRefGoogle Scholar
  18. 18.
    Young, A., Cowan, B., Thrupp, S., Hedley, W., Dell’Italia, L.: Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR Images. Radiology 216(2), 597 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pau Medrano-Gracia
    • 1
  • Avan Suinesiaputra
    • 1
  • Brett Cowan
    • 1
  • David Bluemke
    • 2
  • Alejandro Frangi
    • 3
  • Daniel Lee
    • 4
  • João Lima
    • 5
  • Alistair Young
    • 1
  1. 1.Auckland Bioengineering Inst.University of AucklandNew Zealand
  2. 2.NIH Clinical Ctr.BethesdaUSA
  3. 3.Dept. of Mechanical EngineeringUniversity of SheffieldUnited Kingdom
  4. 4.Feinberg Cardiovascular Research Inst.Northwestern UniversityChicagoUSA
  5. 5.Donald W. Reynolds Research Ctr.Johns Hopkins UniversityBaltimoreUSA

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